A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas
PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperat...
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doaj-5bec828e050042f58f4685dcdb8518b12021-07-29T14:11:07ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-07-011110.3389/fonc.2021.632176632176A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary CarcinomasLei Bi0Lei Bi1Yubo Liu2Jingxu Xu3Ximing Wang4Tong Zhang5Kaiguo Li6Mingguang Duan7Chencui Huang8Xiangjiao Meng9Zhaoqin Huang10Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Radiology, Linyi People’s Hospital, Linyi, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, ChinaDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, ChinaDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaPurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making.https://www.frontiersin.org/articles/10.3389/fonc.2021.632176/fullperiampullary carcinomacomputed tomographyradiomicsnomogramlymph node metastasis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lei Bi Lei Bi Yubo Liu Jingxu Xu Ximing Wang Tong Zhang Kaiguo Li Mingguang Duan Chencui Huang Xiangjiao Meng Zhaoqin Huang |
spellingShingle |
Lei Bi Lei Bi Yubo Liu Jingxu Xu Ximing Wang Tong Zhang Kaiguo Li Mingguang Duan Chencui Huang Xiangjiao Meng Zhaoqin Huang A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas Frontiers in Oncology periampullary carcinoma computed tomography radiomics nomogram lymph node metastasis |
author_facet |
Lei Bi Lei Bi Yubo Liu Jingxu Xu Ximing Wang Tong Zhang Kaiguo Li Mingguang Duan Chencui Huang Xiangjiao Meng Zhaoqin Huang |
author_sort |
Lei Bi |
title |
A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas |
title_short |
A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas |
title_full |
A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas |
title_fullStr |
A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas |
title_full_unstemmed |
A CT-Based Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Periampullary Carcinomas |
title_sort |
ct-based radiomics nomogram for preoperative prediction of lymph node metastasis in periampullary carcinomas |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2021-07-01 |
description |
PurposeTo establish and validate a radiomics nomogram for preoperatively predicting lymph node (LN) metastasis in periampullary carcinomas.Materials and MethodsA total of 122 patients with periampullary carcinoma were assigned into a training set (n = 85) and a validation set (n = 37). The preoperative CT radiomics of all patients were retrospectively assessed and the radiomic features were extracted from portal venous-phase images. The one-way analysis of variance test and the least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature was constructed with logistic regression algorithm, and the radiomics score was calculated. Multivariate logistic regression model integrating independent risk factors was adopted to develop a radiomics nomogram. The performance of the radiomics nomogram was assessed by its calibration, discrimination, and clinical utility with independent validation.ResultsThe radiomics signature, constructed by seven selected features, was closely related to LN metastasis in the training set (p < 0.001) and validation set (p = 0.017). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status demonstrated favorable calibration and discrimination in the training set [area under the curve (AUC), 0.853] and validation set (AUC, 0.853). The decision curve indicated the clinical utility of our nomogram.ConclusionOur CT-based radiomics nomogram, incorporating radiomics signature and CT-reported LN status, could be an individualized and non-invasive tool for preoperative prediction of LN metastasis in periampullary carcinomas, which might assist clinical decision making. |
topic |
periampullary carcinoma computed tomography radiomics nomogram lymph node metastasis |
url |
https://www.frontiersin.org/articles/10.3389/fonc.2021.632176/full |
work_keys_str_mv |
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